






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class     _a							:






  """simple docstring"""





  A_						       = MBartConfig
  A_						       = {}
  A_						       = """gelu"""




  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=7		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=99		,		_UpperCAmelCase=32		,		_UpperCAmelCase=2		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=20		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0		,		)      ->			Union[str, Any]:
     UpperCamelCase_						=  parent
     UpperCamelCase_						=  batch_size
     UpperCamelCase_						=  seq_length
     UpperCamelCase_						=  is_training
     UpperCamelCase_						=  use_labels
     UpperCamelCase_						=  vocab_size
     UpperCamelCase_						=  hidden_size
     UpperCamelCase_						=  num_hidden_layers
     UpperCamelCase_						=  num_attention_heads
     UpperCamelCase_						=  intermediate_size
     UpperCamelCase_						=  hidden_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  max_position_embeddings
     UpperCamelCase_						=  eos_token_id
     UpperCamelCase_						=  pad_token_id
     UpperCamelCase_						=  bos_token_id




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  ids_tensor([self.batch_size, self.seq_length - 1]		,		self.vocab_size			)
     UpperCamelCase_						=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)		,		1			)
     UpperCamelCase_						=  tf.concat([input_ids, eos_tensor]		,		axis=1			)

     UpperCamelCase_						=  ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size			)

     UpperCamelCase_						=  self.config_cls(
         vocab_size=self.vocab_size		,		d_model=self.hidden_size		,		encoder_layers=self.num_hidden_layers		,		decoder_layers=self.num_hidden_layers		,		encoder_attention_heads=self.num_attention_heads		,		decoder_attention_heads=self.num_attention_heads		,		encoder_ffn_dim=self.intermediate_size		,		decoder_ffn_dim=self.intermediate_size		,		dropout=self.hidden_dropout_prob		,		attention_dropout=self.attention_probs_dropout_prob		,		max_position_embeddings=self.max_position_embeddings		,		eos_token_ids=[2]		,		bos_token_id=self.bos_token_id		,		pad_token_id=self.pad_token_id		,		decoder_start_token_id=self.pad_token_id		,		**self.config_updates		,		)
     UpperCamelCase_						=  prepare_mbart_inputs_dict(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)
     return config, inputs_dict




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  TFMBartModel(config=_UpperCAmelCase			).get_decoder()
     UpperCamelCase_						=  inputs_dict['input_ids']

     UpperCamelCase_						=  input_ids[:1, :]
     UpperCamelCase_						=  inputs_dict['attention_mask'][:1, :]
     UpperCamelCase_						=  inputs_dict['head_mask']
     UpperCamelCase_						=  1

     # first forward pass
     UpperCamelCase_						=  model(_UpperCAmelCase		,		attention_mask=_UpperCAmelCase		,		head_mask=_UpperCAmelCase		,		use_cache=_UpperCAmelCase			)

     UpperCamelCase_		,   UpperCamelCase_						=  outputs.to_tuple()
     UpperCamelCase_						=  past_key_values[1]






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=None     ,   __lowercase=None     ,   ):
   if attention_mask is None:
      UpperCamelCase_						=  tf.cast(tf.math.not_equal(__lowercase     ,   config.pad_token_id)     ,   tf.inta)
   if decoder_attention_mask is None:
      UpperCamelCase_						=  tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape     ,   dtype=tf.inta),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,   config.pad_token_id)     ,   tf.inta),
          ]     ,   axis=-1     ,   )
   if head_mask is None:
      UpperCamelCase_						=  tf.ones((config.encoder_layers, config.encoder_attention_heads))
   if decoder_head_mask is None:
      UpperCamelCase_						=  tf.ones((config.decoder_layers, config.decoder_attention_heads))
   if cross_attn_head_mask is None:
      UpperCamelCase_						=  tf.ones((config.decoder_layers, config.decoder_attention_heads))
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  A_						       = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
  A_						       = (
      {
          """conversational""": TFMBartForConditionalGeneration,
          """feature-extraction""": TFMBartModel,
          """summarization""": TFMBartForConditionalGeneration,
          """text2text-generation""": TFMBartForConditionalGeneration,
          """translation""": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  A_						       = True
  A_						       = False
  A_						       = False




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
        # Exception encountered when calling layer '...'
        return True

     return False




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  TFMBartModelTester(self			)
     UpperCamelCase_						=  ConfigTester(self		,		config_class=_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     self.config_tester.run_common_tests()




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     UpperCamelCase_						=  self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase			)




@require_sentencepiece
@require_tokenizers
@require_tf
class     _a							(	unittest.TestCase     ):






  """simple docstring"""





  A_						       = [
      """ UN Chief Says There Is No Military Solution in Syria""",
  ]
  A_						       = [
      """Şeful ONU declară că nu există o soluţie militară în Siria""",
  ]
  A_						       = """facebook/mbart-large-en-ro"""




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			Any:
     return AutoTokenizer.from_pretrained(self.model_name			)




  @cached_property
  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
     return model




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			int:
     UpperCamelCase_						=  self.translate_src_text(**_UpperCAmelCase			)
     self.assertListEqual(self.expected_text		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		**_UpperCAmelCase			)      ->			List[str]:
     UpperCamelCase_						=  self.tokenizer(self.src_text		,		**_UpperCAmelCase		,		return_tensors='tf'			)
     UpperCamelCase_						=  self.model.generate(
         model_inputs.input_ids		,		attention_mask=model_inputs.attention_mask		,		num_beams=2			)
     UpperCamelCase_						=  self.tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
     return generated_words




  @slow
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     self._assert_generated_batch_equal_expected()







"""simple docstring"""



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Dict       		=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a       :						Dict       		=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = state_dict.pop(_A   )
    _UpperCAmelCase        = val


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase        = key.replace("""backbone.0.body"""       ,   """backbone.conv_encoder.model"""   )
            _UpperCAmelCase        = value
        else:
            _UpperCAmelCase        = value

    return new_state_dict


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """"""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase        = state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase        = in_proj_weight_cross_attn[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[:2_5_6]
        _UpperCAmelCase        = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight_cross_attn[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[-2_5_6:]


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = image.size
    _UpperCAmelCase        = max(_A       ,   _A   )
    _UpperCAmelCase        = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
    _UpperCAmelCase        = target_max_size / current_max_size
    _UpperCAmelCase        = image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

    return resized_image


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = F.to_tensor(_A   )
    _UpperCAmelCase        = F.normalize(_A       ,   mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   )
    return image


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    logger.info("""Converting model..."""   )

    # load original state dict
    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_A       ,   _A       ,   _A   )
    _UpperCAmelCase        = rename_backbone_keys(_A   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_A   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase        = """model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
            _UpperCAmelCase        = state_dict.pop(_A   )
            _UpperCAmelCase        = val
    # create HuggingFace model and load state dict
    _UpperCAmelCase        = TableTransformerConfig(
        backbone="""resnet18"""       ,   mask_loss_coefficient=1       ,   dice_loss_coefficient=1       ,   ce_loss_coefficient=1       ,   bbox_loss_coefficient=5       ,   giou_loss_coefficient=2       ,   eos_coefficient=0.4       ,   class_cost=1       ,   bbox_cost=5       ,   giou_cost=2       ,   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = 1_5
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = {0: """table""", 1: """table rotated"""}
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}
    else:
        _UpperCAmelCase        = 1_2_5
        _UpperCAmelCase        = 6
        _UpperCAmelCase        = {
            0: """table""",
            1: """table column""",
            2: """table row""",
            3: """table column header""",
            4: """table projected row header""",
            5: """table spanning cell""",
        }
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}

    _UpperCAmelCase        = DetrImageProcessor(
        format="""coco_detection"""       ,   max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0   )
    _UpperCAmelCase        = TableTransformerForObjectDetection(_A   )
    model.load_state_dict(_A   )
    model.eval()

    # verify our conversion
    _UpperCAmelCase        = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
    _UpperCAmelCase        = hf_hub_download(repo_id="""nielsr/example-pdf"""       ,   repo_type="""dataset"""       ,   filename=_A   )
    _UpperCAmelCase        = Image.open(_A   ).convert("""RGB"""   )
    _UpperCAmelCase        = normalize(resize(_A       ,   _A   )   ).unsqueeze(0   )

    _UpperCAmelCase        = model(_A   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = (1, 1_5, 3)
        _UpperCAmelCase        = torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]   )

    else:
        _UpperCAmelCase        = (1, 1_2_5, 7)
        _UpperCAmelCase        = torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]   )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    print("""Looks ok!"""   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_A   ).mkdir(exist_ok=_A   )
        model.save_pretrained(_A   )
        image_processor.save_pretrained(_A   )

    if push_to_hub:
        # Push model to HF hub
        logger.info("""Pushing model to the hub..."""   )
        _UpperCAmelCase        = (
            """microsoft/table-transformer-detection"""
            if """detection""" in checkpoint_url
            else """microsoft/table-transformer-structure-recognition"""
        )
        model.push_to_hub(_A   )
        image_processor.push_to_hub(_A   )


if __name__ == "__main__":
 a       :						Dict       		=		argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
     type=str,
     choices=[
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
     ],
     help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 a       :						int       		=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)